A mobile-assisted voice condition analysis system for Parkinson's disease: assessment of usability conditions.

BACKGROUND AND OBJECTIVE: Automatic voice condition analysis systems to detect Parkinson's disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. METHODS: A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server-client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. RESULTS: In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. CONCLUSION: The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.

Monitoring Parkinson's disease progression based on recorded speech with missing ordinal responses and replicated covariates.

Monitoring Parkinson's Disease (PD) progression is an important task to improve the life quality of the affected people. This task can be performed by extracting features from voice recordings and applying specifically designed statistical models, leading to systems that improve the ability of monitoring the progression of PD in an objective, remote, non-invasive, fast, and economically sustainable way. An experiment has been conducted with 36 subjects to study the progression of the PD over 4 years by using the Hoehn and Yahr (HY) scale and features extracted from the phonation of the vowel/a/. The collected dataset had many missing data, which should be addressed jointly with the non-decreasing nature of the disease and the within-subject variability due to the use of replicated features. In order to handle these issues, a Hidden Markov model for longitudinal data was designed and implemented by using a data augmentation scheme based on different latent variables. Markov chain Monte Carlo methods were used to generate from the posterior distribution. The proposed approach has been tested on simulated data, providing good accuracy rates in the context of a multiclass problem. It also has been applied to the real data obtained from the conducted experiment, providing imputed and predicted HY stages compatible with the progression of PD. The conducted experiment and the proposed approach contribute to fill a gap in the scientific literature on experiments and methodologies for tracking PD progression based on acoustic features and the HY scale. This would help to derive an expert system that can be integrated into the protocols of neurology units in hospital centers.

Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings.

Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system.

A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson's disease.

BACKGROUND AND OBJECTIVE: A new expert system is proposed to discriminate healthy people from people with Parkinson's Disease (PD) in early stages by using Diadochokinesis tests. METHODS: The system is based on temporal and spectral features extracted from the Voice Onset Time (VOT) segments of /ka/ syllables, whose boundaries are delimited by a novel algorithm. For comparison purposes, the approach is applied also to /pa/ and /ta/ syllables. In order to develop and validate the system, a voice recording database composed of 27 individuals diagnosed with PD and 27 healthy controls has been collected. This database reflects an average disease stage of 1.85 ±â€¯0.55 according to Hoehn and Yahr scale. System design is based on feature extraction, feature selection and Support Vector Machine learning. RESULTS: The novel VOT algorithm, based on a simple and computationally efficient approach, demonstrates accurate estimation of VOT boundaries on /ka/ syllables for both healthy and PD-affected speakers. The PD detection approach based on /k/ plosive consonant achieves the highest discrimination capability (92.2% using 10-fold cross-validation and 94.4% in the case of leave-one-out method) in comparison to the corresponding versions based on the other two plosives (/p/ and /t/). CONCLUSION: A high accuracy has been obtained on a database with a lower average disease stage than previous articulatory databases presented in the literature.

A two-stage variable selection and classification approach for Parkinson's disease detection by using voice recording replications.

BACKGROUND AND OBJECTIVE: In the scientific literature, there is a lack of variable selection and classification methods considering replicated data. The problem motivating this work consists in the discrimination of people suffering Parkinson's disease from healthy subjects based on acoustic features automatically extracted from replicated voice recordings. METHODS: A two-stage variable selection and classification approach has been developed to properly match the replication-based experimental design. The way the statistical approach has been specified allows that the computational problems are solved by using an easy-to-implement Gibbs sampling algorithm. RESULTS: The proposed approach produces an acceptable predictive capacity for PD discrimination with the considered database, despite the fact that the sample size is relatively small. Specifically, the accuracy rate, sensitivity and specificity are 86.2%, 82.5%, and 90.0%, respectively. However, the most important fact is that there is an improvement in the interpretability of the results at the same time that it is shown a better chain mixing and a lower computation time with respect to the only-classification approaches presented in the scientific literature. CONCLUSIONS: To the best of the authors' knowledge, this is the first approach developed to properly consider intra-subject variability for variable selection and classification. Although the proposed approach has been applied for PD discrimination, it can be applied in other contexts with similar replication-based experimental designs.